In this paper we analyse the effects of changes in illumination, pose, image size, facial area size, image quality and resolution on the face recognition performance of a state-of-the-art deep neural network. The main focus of this work lies in the performance of the VGG model, a 16-layer convolutional neural network and its ability to correctly classify several pairs of test images as clients or imposters. For some of the tests, we provide methods that simulate image acquisition in unstable and uncontrolled environments and discuss the results. Effects of blurring, cropping and resizing facial images could be understood as a result of a less powerful image acquisition system in terms of quality, the use of which consequently reduces the costs of a day to day face recognition application. We show that the said system performs well in such conditions. The effects of illumination and pose on face recognition accuracy are analysed using EYB and FERET datasets respectively. The study of other said changes of environmental variables are analysed on the LFW database and for which we pre-prepare several subsets, where we steadily deteriorate the conditions of client's probe images compared to the gallery image and measure the distance between its feature vectors using the cosine distance.